Clear environment

rm(list = ls())

Load required packages

Packages have been installed with mamba into a specific, special conda environment set up to run RStudio sessions

library("DESeq2")
library("tidyverse")
library("EnhancedVolcano")
library("ggpubr")
library("scales")
library("grid")

Set up variables for paths

p_repo <- "~/projects-etc/2022_transcriptome-construction"
p_res <- "results/2022-1025"
p_txt <- "Alison/txt"

p_full <- paste(p_repo, p_res, p_txt, sep = "/")
dir.exists(p_full)

p_Alison <- gsub('(.*)/\\w+', '\\1', p_full)
dir.exists(p_Alison)

p_save <- paste(p_repo, p_res, sep = "/")

Read in count and metadata for mRNA

countData_Nascent <- read.delim(
    paste(p_full, "mRNA_Nascent_Nab3.txt", sep = "/"),
    header = TRUE
)
metadata_Nascent <- read.delim(
    paste(p_full, "deletionmeta.txt", sep = "/"),
    header = TRUE
)

Make DESEQDataSet Object

dds_n <- DESeq2::DESeqDataSetFromMatrix(
    countData=countData_Nascent,
    colData=metadata_Nascent, 
    design= ~ dex,
    tidy = TRUE
)
dds_n
# class: DESeqDataSet 
# dim: 6600 5 
# metadata(1): version
# assays(1): counts
# rownames(6600): Q0010 Q0017 ... YPR204C-A YPR204W
# rowData names(0):
# colnames(5): Q_6125_N Q_7718_N Q_6126_N Q_7716_N Q_7714_N
# colData names(2): id dex

Set size factors (from spike in)

BiocGenerics::sizeFactors(dds_n) <- c(1, 1.188, 1, 2.097, 1.662)

#QUESTION Is this where we want the estimated size factors to be input? #QUESTION Is the current means of calculating them appropriate for the DESeq2 model?

Run DESEQ Function

dds_n <- DESeq2::DESeq(dds_n)

Look at results table

res_n <- DESeq2::results(dds_n)
DESeq2::results(dds_n, tidy = TRUE) %>% head()

Summary of differential gene expression

summary(res_n)
# out of 6488 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 2111, 33%
# LFC < 0 (down)     : 872, 13%
# outliers [1]       : 1, 0.015%
# low counts [2]     : 0, 0%
# (mean count < 0)
# [1] see 'cooksCutoff' argument of ?results
# [2] see 'independentFiltering' argument of ?results

Sort Summary List by adjusted p-value

res_n <- res_n[order(res_n$padj), ]
head(res_n)

Make volcano

EnhancedVolcano::EnhancedVolcano(
    res_n,
    lab = NA,
    x = 'log2FoldChange',
    y = 'pvalue'
)

Make volcano and save png

dir.exists(paste(p_save, "png", sep = "/")) ||
    dir.create(paste(p_save, "png", sep = "/"))

png <- EnhancedVolcano::EnhancedVolcano(
    res_n,
    lab = NA,
    x = 'log2FoldChange',
    y = 'padj',
    xlab = bquote(~Log[2]~ 'fold change'),
    title = 'Sense Nascent Transcription', 
    gridlines.major = FALSE,
    gridlines.minor = FALSE,
    pCutoff = 0.05,
    FCcutoff = 2
)

timestampedFilename <- paste0(
    paste(p_save, "png", sep = "/"), "/",
    "sense_differential_nascent_expression", ".",
    format(Sys.time(), format = "%F_%H%M%S"), ".png"
)
ggplot2::ggsave(timestampedFilename, plot = png)

Write DESeq2 to matrix

dir.exists(paste(p_save, "txt", sep = "/")) ||
    dir.create(paste(p_save, "txt", sep = "/"))

p_txt <- paste(p_save, "txt", sep = "/")
write.table(
    tibble::rownames_to_column(as.data.frame(res_n), "gene"),
    file = paste(p_txt, "nascent_mRNA_diff_res.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    tibble::rownames_to_column(
        as.data.frame(res_n[order(res_n$padj), ]), "gene"
    ),
    file = paste(p_txt, "nascent_mRNA_diff_res_ordered.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

#QUESTION I then go in excel and name the gene category “name” Why does R not name this column? I wish I knew

#ANSWER write.table is printing the row names which, by default, don’t have column names; the row names become the first column for all lines after the initial one; see the way I addressed this above

Read ordered DESeq2 matrix back in

#COMMENT There has gotta be a better way to do this

#ANSWER There is: see below

res1_n <- tibble::rownames_to_column(
    as.data.frame(res_n[order(res_n$padj), ]), "gene"
)

#filter results

res1Nab3_up <- dplyr::filter(res1_n, log2FoldChange > 2, padj < 0.05)
res1Nab3_down <- dplyr::filter(res1_n, log2FoldChange < -2, padj < 0.05)

#write filtered results for go terms

write.table(
    res1Nab3_up,
    file = paste(p_txt, "res1Nab3_up_SENSE.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    res1Nab3_down,
    file = paste(p_txt, "res1Nab3_down_SENSE.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

#filter results

res1Nab3_up_1p5 <- filter(res1_n, log2FoldChange > 1.5, padj < 0.05)
res1Nab3_down_1p5 <- filter(res1_n, log2FoldChange < -1.5, padj < 0.05)

#read in AS count and metadata

AS_countData_Nascent <- read.delim(
    paste(p_full, "AS_mRNA_Nascent_Nab3.txt", sep = "/"),
    header = TRUE
)
AS_metadata_Nascent <- read.delim(
    paste(p_full, "deletionmeta_AS.txt", sep = "/"),
    header = TRUE
)

Make DESEQDataSet Object AS

dds_n_as<- DESeq2::DESeqDataSetFromMatrix(
    countData = AS_countData_Nascent,
    colData = AS_metadata_Nascent, 
    design= ~ dex,
    tidy = TRUE
)
dds_n_as

#size factors #from spike in

BiocGenerics::sizeFactors(dds_n_as)  <-  c(1, 1.188, 1, 2.097, 1.662)

Run DESEQ Function

dds_n_as <- DESeq2::DESeq(dds_n_as)

Look at results table

res_n_as<- DESeq2::results(dds_n_as)
DESeq2::results(dds_n_as, tidy=TRUE) %>% head()

Summary of differential gene expression

summary(res_n_as)

##Sort Summary List by p-value

res_n_as <- res_n_as[order(res_n_as$padj), ]
head(res_n_as)

#make volcano plot - saves png

png <- EnhancedVolcano::EnhancedVolcano(
    res_n_as,
    lab = NA,
    x = 'log2FoldChange',
    y = 'padj',
    xlab = bquote(~Log[2]~ 'fold change'),
    title = 'Antisense Nascent Transcription', 
    gridlines.major = FALSE,
    gridlines.minor = FALSE,
    pCutoff = 0.05,
    FCcutoff = 2
)

timestampedFilename <- paste0(
    paste(p_save, "png", sep = "/"), "/",
    "AS_differential_nascent_expression", ".",
    format(Sys.time(), format = "%F_%H%M%S"), ".png"
)
ggplot2::ggsave(timestampedFilename, plot = png)

#write results file AS

write.table(
    tibble::rownames_to_column(as.data.frame(res_n_as), "gene"),
    file = paste(p_txt, "AS_Nascent_mRNA_diff_res_sf.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    tibble::rownames_to_column(
        as.data.frame(res_n_as[order(res_n_as$padj), ]), "gene"
    ),
    file = paste(p_txt, "AS_Nascent_mRNA_diff_res_sf_ordered.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)
res1_n_as <- tibble::rownames_to_column(
    as.data.frame(res_n_as[order(res_n_as$padj), ]), "gene"
)

#filter AS results

AS_res1Nab3_up <- dplyr::filter(res1_n_as, log2FoldChange > 2, padj < 0.05)
AS_res1Nab3_down <- dplyr::filter(res1_n_as, log2FoldChange < -2, padj < 0.05)

#write AS to file

write.table(
    AS_res1Nab3_up, 
    file = paste(p_txt, "AS_res1Nab3_up.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    AS_res1Nab3_down, 
    file = paste(p_txt, "AS_res1Nab3_down.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

#filter AS results different

AS_res1Nab3_up_1p5 <- dplyr::filter(res1_n_as, log2FoldChange > 1.5, padj < 0.05)
AS_res1Nab3_down_1p5 <- dplyr::filter(res1_n_as, log2FoldChange < -1.5, padj < 0.05)

#overlap between sense and AS by name

Nas_AS_up <- dplyr::inner_join(
    AS_res1Nab3_up,
    res1Nab3_up,
    by = "gene"
)
Nas_AS_down <- dplyr::inner_join(
    AS_res1Nab3_down,
    res1Nab3_down,
    by = "gene"
)
Nas_AS_down_up <- dplyr::inner_join(
    AS_res1Nab3_down,
    res1Nab3_up,
    by = "gene"
)
Nas_AS_up_down <- dplyr::inner_join(
    AS_res1Nab3_up,
    res1Nab3_down,
    by = "gene"
)

#overlap between sense and AS by name - different gates

Nas_1.5_AS_up <- dplyr::inner_join(
    AS_res1Nab3_up_1p5,
    res1Nab3_up_1p5,
    by = "gene"
)
Nas_1.5_AS_down <- dplyr::inner_join(
    AS_res1Nab3_down_1p5,
    res1Nab3_down_1p5,
    by = "gene"
)
Nas_1.5_AS_down_up <- dplyr::inner_join(
    AS_res1Nab3_down_1p5,
    res1Nab3_up_1p5,
    by = "gene"
)
Nas_1.5_AS_up_down <- dplyr::inner_join(
    AS_res1Nab3_up_1p5,
    res1Nab3_down_1p5,
    by = "gene"
)

#read in AS of Nab3 TPM - from replicate analysis rmd

AS_TPM <- read.delim(
    paste(p_full, "AS_TPM.txt", sep = "/"),
    header = TRUE
)

#calc average TPM


AS_TPM <- AS_TPM %>%
    dplyr::rowwise() %>%
    dplyr::mutate(
        Avg_single_Tag_AS_TPM = mean(c(Q_7718_N_AS, Q_7718_N_AS), na.rm = T)
    ) 
AS_TPM <- AS_TPM %>%
    dplyr::rowwise() %>%
    dplyr::mutate(
        Avg_parental_AS_TPM = mean(c(Q_6125_N_AS, Q_6126_N_AS), na.rm = T)
    )

#extract only relevent columns

AS_TPM_Clean <- AS_TPM %>%
    dplyr::select(c("name", "Avg_single_Tag_AS_TPM", "Avg_parental_AS_TPM"))

#  Change the "name" column to "gene" column
colnames(AS_TPM_Clean)[colnames(AS_TPM_Clean) == "name"] <- "gene"

#access quantiles

quantile(AS_TPM_Clean$Avg_single_Tag_AS_TPM) 
quantile(AS_TPM_Clean$Avg_parental_AS_TPM) 

#more fine grain

quantile(AS_TPM_Clean$Avg_single_Tag_AS_TPM, probs = c(0.8,0.85, .9, .95)) 
quantile(AS_TPM_Clean$Avg_parental_AS_TPM, probs = c(0.8,0.85, .9, .95)) 

#highest 20% of AS by absolute level

Cuttoff_TPM_AS <- dplyr::filter(AS_TPM_Clean, Avg_single_Tag_AS_TPM > 14)

#now were doing the intersection of AS of high level and differential expression

TPM_Nas_AS_up <- dplyr::inner_join(AS_res1Nab3_up, Cuttoff_TPM_AS, by = "gene")
nrow(TPM_Nas_AS_up)

#731 AS up

#overlap between TPM_Nas_AS_up and res1Nab3_down_1p5 (one way to gate functional AS)

TPM_AS_up_Sense_down <- dplyr::inner_join(
    TPM_Nas_AS_up,
    res1Nab3_down_1p5,
    by = "gene"
)
nrow(TPM_AS_up_Sense_down)
nrow(res1Nab3_down_1p5)

#overlap between TPM_Nas_AS_up and res1Nab3_down (different way to gate functional AS)

TPM_AS_up_Sense_down_2 <- dplyr::inner_join(
    TPM_Nas_AS_up,
    res1Nab3_down,
    by = "gene"
)
nrow(TPM_AS_up_Sense_down_2)
nrow(res1Nab3_down)

#rename AS table names so I can compare them with sense values - they will then have different names and labels

colnames(res1_n_as) <- c(
    "gene", "AS_baseMean", "AS_log2FoldChange", "AS_lfcSE",
    "AS_stat", "AS_pvalue", "AS_padj"
)

#one giant matrix of both sense and Antisense

Combinded_RES <- merge(res1_n, res1_n_as, by = "gene")

#graph logfoldchange of sense and Antisense #“pairwise.complete.obs” must be used to throw out incomplete values - I know some authors will change every zero to 1 to fix this

grob1 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation: ",
            round(cor(
                Combinded_RES$log2FoldChange,
                Combinded_RES$AS_log2FoldChange,
                use = "pairwise.complete.obs"),
            4)
        ),
        x = 0.5,
        y = 0.97,
        hjust = 0,
        gp = gpar(col = 'blue', fontsize = 11, fontface = "bold")
    )
)

ggplot2::ggplot(data = Combinded_RES) + 
    ggplot2::geom_vline(xintercept = 0, alpha = .5) +
    ggplot2::geom_hline(yintercept = 0, alpha = .5) +
    ggplot2::geom_point(
        data = Combinded_RES,
        mapping = aes(y = log2FoldChange, x = AS_log2FoldChange),
        alpha = 4/10,
        size = 1,
        color = 'black'
    ) +
    ggplot2::geom_smooth(
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'forestgreen'
    ) +
    ggplot2::theme_bw() +
    ggplot2::annotation_custom(grob1) +
    ggplot2::geom_smooth(
        data = Combinded_RES,
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'blue'
    )

#check colnames of TPM_Nas_AS_up

colnames(TPM_Nas_AS_up)

#rename AS table names so I can compare them with sense values - they will then have different names and labels

colnames(TPM_Nas_AS_up) <- c(
    "gene", "AS_baseMean", "AS_log2FoldChange", "AS_lfcSE", "AS_stat",
    "AS_pvalue", "AS_padj", "Avg_single_Tag_AS_TPM", "Avg_parental_AS_TPM"
)

#add sense expression info onto highest AS table so those points can be graphed seperately

High_up_AS <- merge(TPM_Nas_AS_up, res1_n, by = "gene")

#scatterplot now with high AS highlighted in red

grob2 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation Over Expressed AS: ",
            round(
                cor(
                    High_up_AS$log2FoldChange,
                    High_up_AS$AS_log2FoldChange,
                    use = "pairwise.complete.obs"),
                4
            )
        ),
        x= 0.3,
        y = 0.93,
        hjust = 0,
        gp = gpar(col = 'red', fontsize = 11, fontface = "bold")
    )
)

ggplot2::ggplot(data = Combinded_RES) + 
    ggplot2::geom_vline(xintercept = 0, alpha = .5) +
    ggplot2::geom_hline(yintercept = 0, alpha = .5) +
    ggplot2::geom_point(
        data = Combinded_RES,
        mapping = aes(y = log2FoldChange, x = AS_log2FoldChange),
        alpha = 4/10,
        size = 1,
        color = 'black'
    ) +
    ggplot2::geom_smooth(
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'forestgreen'
    ) +
    ggplot2::theme_bw() +
    ggplot2::geom_point(
        data = High_up_AS, 
        aes(y = log2FoldChange,x=AS_log2FoldChange), 
        color = "red",
        size =1, 
        alpha = 4/10
    )+
    ggplot2::annotation_custom(grob1) +
    ggplot2::annotation_custom(grob2) +
    ggplot2::geom_smooth(
        data = Combinded_RES,
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'blue'
    ) +
    ggplot2::geom_smooth(
        data = High_up_AS,
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = "red"
    )

now it is time to do grouped analysis :)

#Make a new variable that’s the same thing (need this to remove genes, so no gene is asigned to more than one group)

#now we are binning genes by group of mRNA expression #group 1 up genes #group 5 down genes

#group 3 “neutral”

#remove group 1 genes from DeSeq2_Sense_Results

nrow(DeSeq2_Sense_Results) + nrow(grp1_DeSeq2_Sense_Results)
[1] 6600

#remove group 5 genes from DeSeq2_Sense_Results

nrow(grp1_DeSeq2_Sense_Results) + nrow(grp5_DeSeq2_Sense_Results)
[1] 808

#remove group 3 genes from DeSeq2_Sense_Results

nrow(grp3_DeSeq2_Sense_Results)
[1] 4418

#sanity check count rows

nrow(grp5_DeSeq2_Sense_Results)
[1] 72

#now from not group 1,3,5 - split into group 2 and 4

#sanity check count rows

nrow(grp1_DeSeq2_Sense_Results) +
nrow(grp5_DeSeq2_Sense_Results) +
nrow(grp3_DeSeq2_Sense_Results) +
nrow(grp2_DeSeq2_Sense_Results) +
nrow(grp4_DeSeq2_Sense_Results)
[1] 6488

#sanity check count rows

nrow(grp1_DeSeq2_Sense_Results) + nrow(grp5_DeSeq2_Sense_Results)
[1] 808

#sanity check count rows #only thing left in “DeSeq2_Sense_Results” is NA results

nrow(DeSeq2_Sense_Results)
[1] 112

#now we have groups by sense expression change #now make table to integrate AS expression info

#what happens next is how i make violin plots. #I figured this out like a year ago and have copy pasted this code a bunch. #this is the bins

#now the little seperate bins are glued together

#now we we make a violin plot

#now we we make a violin plot with p values

#now were going to do the same thing #the bins stay the same #but in the bins were are putting AS expression level

colnames(TPM_group1)
 [1] "gene"                  "baseMean"              "log2FoldChange"        "lfcSE"                 "stat"                  "pvalue"               
 [7] "padj"                  "Q_6125_N_AS"           "Q_7718_N_AS"           "Q_6126_N_AS"           "Q_7716_N_AS"           "length_kb"            
[13] "ratio_6125"            "ratio_6126"            "Avg_single_Tag_AS_TPM" "Avg_parental_AS_TPM"  

#making data frames to put into violin plot

#glueing the data frames into one

#make violin plot

#make violin plot but now with p values

#now I am gating by fold change >2 instead of 4

#comparing between fold change >2 instead of 4

nrow(ONE_TPM_Nas_AS_up)
[1] 878

#New Sense filters

#new functional RNA catagory

nrow(ONE_UpAS_DownMRNA)
[1] 187

#write names only to file #thanks for writing this Kris

close(fileConn)
Error in UseMethod("close") : 
  no applicable method for 'close' applied to an object of class "character"

#calc overlaps

nrow(ONE_res1Nab3_up)
[1] 1496

#HERE ##now I am going to compare to 5781 and 2 WT Q and G1 #so I gotta read that data in

#filtered by expression

#calc what high AS is

quantile(WT_TPM$Avg_Q_IP_ANTISense, probs = c(0.5, 0.75, 0.8,0.85, 0.9, 0.95)) 
      50%       75%       80%       85%       90%       95% 
 3.369446 10.541939 14.414169 21.709871 36.698259 79.249805 

#once again I am gating at 80%

#check colnames

colnames(WT_high_level_AS)
 [1] "ensgene"             "s5781_G1_IP"         "s5781_Q_IP"          "s5782_G1_IP"         "s5782_Q_IP"          "s5781_G1_IP_AS"      "s5781_Q_IP_AS"      
 [8] "s5782_G1_IP_AS"      "s5782_Q_IP_AS"       "length_kb"           "Avg_Q_IP_Sense"      "Avg_G1_IP_Sense"     "Avg_Q_IP_ANTISense"  "Avg_G1_IP_ANTISense"

#merge high level and overexpressed

#now compare WT to Nab3 experiment

nrow(High_up_AS)
[1] 731

#of the 180 above - how many are functional?

nrow(AS_tested)
[1] 180

#making a list of gene names from Nab3 data so I can highlight those genes in WT data

nrow(ONE_TPM_Nas_AS_up_list)
[1] 878

#all genes in WT - AS tpm and differential expression

#graph it and it is a blob

#map Nab3 genes to WT data

#graph it and it is 2 blobs!! :)

#graph it again

#all on one. busy, very hard to look at.

---
title: "R Notebook"
output: html_notebook
---

---
title: "R Notebook"
output: html_notebook
---
# Clear environment
```{r}
rm(list = ls())
```

# Load required packages
## Packages have been installed with mamba into a specific, special conda environment set up to run RStudio sessions
```{r}
library("DESeq2")
library("tidyverse")
library("EnhancedVolcano")
library("ggpubr")
library("scales")
library("grid")
```

# Set up variables for paths
```{r}
p_repo <- "~/projects-etc/2022_transcriptome-construction"
p_res <- "results/2022-1025"
p_txt <- "Alison/txt"

p_full <- paste(p_repo, p_res, p_txt, sep = "/")
dir.exists(p_full)

p_Alison <- gsub('(.*)/\\w+', '\\1', p_full)
dir.exists(p_Alison)

p_save <- paste(p_repo, p_res, sep = "/")
```

# Read in count and metadata for mRNA
```{r}
countData_Nascent <- read.delim(
    paste(p_full, "mRNA_Nascent_Nab3.txt", sep = "/"),
    header = TRUE
)
metadata_Nascent <- read.delim(
    paste(p_full, "deletionmeta.txt", sep = "/"),
    header = TRUE
)
```


## Make DESEQDataSet Object
```{r}
dds_n <- DESeq2::DESeqDataSetFromMatrix(
    countData=countData_Nascent,
    colData=metadata_Nascent, 
    design= ~ dex,
    tidy = TRUE
)
dds_n
# class: DESeqDataSet 
# dim: 6600 5 
# metadata(1): version
# assays(1): counts
# rownames(6600): Q0010 Q0017 ... YPR204C-A YPR204W
# rowData names(0):
# colnames(5): Q_6125_N Q_7718_N Q_6126_N Q_7716_N Q_7714_N
# colData names(2): id dex
```

## Set size factors (from spike in)
```{r}
BiocGenerics::sizeFactors(dds_n) <- c(1, 1.188, 1, 2.097, 1.662)
```

`#QUESTION` Is this where we want the estimated size factors to be input?
`#QUESTION` Is the current means of calculating them appropriate for the DESeq2 model?

## Run DESEQ Function
```{r}
dds_n <- DESeq2::DESeq(dds_n)
```

## Look at results table
```{r}
res_n <- DESeq2::results(dds_n)
DESeq2::results(dds_n, tidy = TRUE) %>% head()
```


## Summary of differential gene expression
```{r}
summary(res_n)
# out of 6488 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 2111, 33%
# LFC < 0 (down)     : 872, 13%
# outliers [1]       : 1, 0.015%
# low counts [2]     : 0, 0%
# (mean count < 0)
# [1] see 'cooksCutoff' argument of ?results
# [2] see 'independentFiltering' argument of ?results
```


### Sort Summary List by adjusted p-value
```{r}
res_n <- res_n[order(res_n$padj), ]
head(res_n)
```

### Make volcano
```{r}
EnhancedVolcano::EnhancedVolcano(
    res_n,
    lab = NA,
    x = 'log2FoldChange',
    y = 'pvalue'
)
```

### Make volcano and save png
```{r}
dir.exists(paste(p_save, "png", sep = "/")) ||
    dir.create(paste(p_save, "png", sep = "/"))

png <- EnhancedVolcano::EnhancedVolcano(
    res_n,
    lab = NA,
    x = 'log2FoldChange',
    y = 'padj',
    xlab = bquote(~Log[2]~ 'fold change'),
    title = 'Sense Nascent Transcription', 
    gridlines.major = FALSE,
    gridlines.minor = FALSE,
    pCutoff = 0.05,
    FCcutoff = 2
)

timestampedFilename <- paste0(
    paste(p_save, "png", sep = "/"), "/",
    "sense_differential_nascent_expression", ".",
    format(Sys.time(), format = "%F_%H%M%S"), ".png"
)
ggplot2::ggsave(timestampedFilename, plot = png)
```

### Write DESeq2 to matrix
```{r}
dir.exists(paste(p_save, "txt", sep = "/")) ||
    dir.create(paste(p_save, "txt", sep = "/"))

p_txt <- paste(p_save, "txt", sep = "/")
write.table(
    tibble::rownames_to_column(as.data.frame(res_n), "gene"),
    file = paste(p_txt, "nascent_mRNA_diff_res.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    tibble::rownames_to_column(
        as.data.frame(res_n[order(res_n$padj), ]), "gene"
    ),
    file = paste(p_txt, "nascent_mRNA_diff_res_ordered.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)
```
`#QUESTION` I then go in excel and name the gene category "name" Why does R not name this column? I wish I knew

`#ANSWER` `write.table` is printing the row names which, by default, don't have column names; the row names become the first column for all lines after the initial one; see the way I addressed this above

### Read ordered DESeq2 matrix back in
`#COMMENT` There has gotta be a better way to do this

`#ANSWER` There is: see below
```{r}
res1_n <- tibble::rownames_to_column(
    as.data.frame(res_n[order(res_n$padj), ]), "gene"
)
```

#filter results
```{r}
res1Nab3_up <- dplyr::filter(res1_n, log2FoldChange > 2, padj < 0.05)
res1Nab3_down <- dplyr::filter(res1_n, log2FoldChange < -2, padj < 0.05)
```

#write filtered results for go terms
```{r}
write.table(
    res1Nab3_up,
    file = paste(p_txt, "res1Nab3_up_SENSE.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    res1Nab3_down,
    file = paste(p_txt, "res1Nab3_down_SENSE.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)
```

#filter results
```{r}
res1Nab3_up_1p5 <- filter(res1_n, log2FoldChange > 1.5, padj < 0.05)
res1Nab3_down_1p5 <- filter(res1_n, log2FoldChange < -1.5, padj < 0.05)
```


#read in AS count and metadata
```{r}
AS_countData_Nascent <- read.delim(
    paste(p_full, "AS_mRNA_Nascent_Nab3.txt", sep = "/"),
    header = TRUE
)
AS_metadata_Nascent <- read.delim(
    paste(p_full, "deletionmeta_AS.txt", sep = "/"),
    header = TRUE
)
```


## Make DESEQDataSet Object AS
```{r}
dds_n_as<- DESeq2::DESeqDataSetFromMatrix(
    countData = AS_countData_Nascent,
    colData = AS_metadata_Nascent, 
    design= ~ dex,
    tidy = TRUE
)
dds_n_as
```

#size factors
#from spike in
```{r}
BiocGenerics::sizeFactors(dds_n_as)  <-  c(1, 1.188, 1, 2.097, 1.662)
```


## Run DESEQ Function
```{r}
dds_n_as <- DESeq2::DESeq(dds_n_as)
```


## Look at results table
```{r}
res_n_as<- DESeq2::results(dds_n_as)
DESeq2::results(dds_n_as, tidy=TRUE) %>% head()
```


## Summary of differential gene expression
```{r}
summary(res_n_as)
```


##Sort Summary List by p-value
```{r}
res_n_as <- res_n_as[order(res_n_as$padj), ]
head(res_n_as)
```

#make volcano plot - saves png 
```{r}
png <- EnhancedVolcano::EnhancedVolcano(
    res_n_as,
    lab = NA,
    x = 'log2FoldChange',
    y = 'padj',
    xlab = bquote(~Log[2]~ 'fold change'),
    title = 'Antisense Nascent Transcription', 
    gridlines.major = FALSE,
    gridlines.minor = FALSE,
    pCutoff = 0.05,
    FCcutoff = 2
)

timestampedFilename <- paste0(
    paste(p_save, "png", sep = "/"), "/",
    "AS_differential_nascent_expression", ".",
    format(Sys.time(), format = "%F_%H%M%S"), ".png"
)
ggplot2::ggsave(timestampedFilename, plot = png)
```

#write results file AS
```{r}
write.table(
    tibble::rownames_to_column(as.data.frame(res_n_as), "gene"),
    file = paste(p_txt, "AS_Nascent_mRNA_diff_res_sf.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    tibble::rownames_to_column(
        as.data.frame(res_n_as[order(res_n_as$padj), ]), "gene"
    ),
    file = paste(p_txt, "AS_Nascent_mRNA_diff_res_sf_ordered.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)
```

```{r}
res1_n_as <- tibble::rownames_to_column(
    as.data.frame(res_n_as[order(res_n_as$padj), ]), "gene"
)
```

#filter AS results 
```{r}
AS_res1Nab3_up <- dplyr::filter(res1_n_as, log2FoldChange > 2, padj < 0.05)
AS_res1Nab3_down <- dplyr::filter(res1_n_as, log2FoldChange < -2, padj < 0.05)
```

#write AS to file 
```{r}
write.table(
    AS_res1Nab3_up, 
    file = paste(p_txt, "AS_res1Nab3_up.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)

write.table(
    AS_res1Nab3_down, 
    file = paste(p_txt, "AS_res1Nab3_down.txt", sep = "/"),
    sep = "\t",
    row.names = FALSE
)
```


#filter AS results different 
```{r}
AS_res1Nab3_up_1p5 <- dplyr::filter(res1_n_as, log2FoldChange > 1.5, padj < 0.05)
AS_res1Nab3_down_1p5 <- dplyr::filter(res1_n_as, log2FoldChange < -1.5, padj < 0.05)
```


#overlap between sense and AS by name 
```{r}
Nas_AS_up <- dplyr::inner_join(
    AS_res1Nab3_up,
    res1Nab3_up,
    by = "gene"
)
Nas_AS_down <- dplyr::inner_join(
    AS_res1Nab3_down,
    res1Nab3_down,
    by = "gene"
)
Nas_AS_down_up <- dplyr::inner_join(
    AS_res1Nab3_down,
    res1Nab3_up,
    by = "gene"
)
Nas_AS_up_down <- dplyr::inner_join(
    AS_res1Nab3_up,
    res1Nab3_down,
    by = "gene"
)
```

#overlap between sense and AS by name - different gates 
```{r}
Nas_1.5_AS_up <- dplyr::inner_join(
    AS_res1Nab3_up_1p5,
    res1Nab3_up_1p5,
    by = "gene"
)
Nas_1.5_AS_down <- dplyr::inner_join(
    AS_res1Nab3_down_1p5,
    res1Nab3_down_1p5,
    by = "gene"
)
Nas_1.5_AS_down_up <- dplyr::inner_join(
    AS_res1Nab3_down_1p5,
    res1Nab3_up_1p5,
    by = "gene"
)
Nas_1.5_AS_up_down <- dplyr::inner_join(
    AS_res1Nab3_up_1p5,
    res1Nab3_down_1p5,
    by = "gene"
)
```

#read in AS of Nab3 TPM - from replicate analysis rmd 
```{r}
AS_TPM <- read.delim(
    paste(p_full, "AS_TPM.txt", sep = "/"),
    header = TRUE
)
```

#calc average TPM 
```{r}

AS_TPM <- AS_TPM %>%
    dplyr::rowwise() %>%
    dplyr::mutate(
        Avg_single_Tag_AS_TPM = mean(c(Q_7718_N_AS, Q_7718_N_AS), na.rm = T)
    ) 
AS_TPM <- AS_TPM %>%
    dplyr::rowwise() %>%
    dplyr::mutate(
        Avg_parental_AS_TPM = mean(c(Q_6125_N_AS, Q_6126_N_AS), na.rm = T)
    )

```

#extract only relevent columns 
```{r}
AS_TPM_Clean <- AS_TPM %>%
    dplyr::select(c("name", "Avg_single_Tag_AS_TPM", "Avg_parental_AS_TPM"))

#  Change the "name" column to "gene" column
colnames(AS_TPM_Clean)[colnames(AS_TPM_Clean) == "name"] <- "gene"
```

#access quantiles 
```{r}
quantile(AS_TPM_Clean$Avg_single_Tag_AS_TPM) 
quantile(AS_TPM_Clean$Avg_parental_AS_TPM) 
```

#more fine grain 
```{r}
quantile(AS_TPM_Clean$Avg_single_Tag_AS_TPM, probs = c(0.8,0.85, .9, .95)) 
quantile(AS_TPM_Clean$Avg_parental_AS_TPM, probs = c(0.8,0.85, .9, .95)) 
```

#highest 20% of AS by absolute level 
```{r}
Cuttoff_TPM_AS <- dplyr::filter(AS_TPM_Clean, Avg_single_Tag_AS_TPM > 14)
```

#now were doing the intersection of AS of high level and differential expression 
```{r}
TPM_Nas_AS_up <- dplyr::inner_join(AS_res1Nab3_up, Cuttoff_TPM_AS, by = "gene")
nrow(TPM_Nas_AS_up)
```
#731 AS up 

#overlap between TPM_Nas_AS_up and res1Nab3_down_1p5 (one way to gate functional AS)
```{r}
TPM_AS_up_Sense_down <- dplyr::inner_join(
    TPM_Nas_AS_up,
    res1Nab3_down_1p5,
    by = "gene"
)
nrow(TPM_AS_up_Sense_down)
nrow(res1Nab3_down_1p5)
```

#overlap between TPM_Nas_AS_up and res1Nab3_down (different way to gate functional AS)
```{r}
TPM_AS_up_Sense_down_2 <- dplyr::inner_join(
    TPM_Nas_AS_up,
    res1Nab3_down,
    by = "gene"
)
nrow(TPM_AS_up_Sense_down_2)
nrow(res1Nab3_down)
```

#rename AS table names so I can compare them with sense values - they will then have different names and labels 
```{r}
colnames(res1_n_as) <- c(
    "gene", "AS_baseMean", "AS_log2FoldChange", "AS_lfcSE",
    "AS_stat", "AS_pvalue", "AS_padj"
)
```

#one giant matrix of both sense and Antisense 
```{r}
Combinded_RES <- merge(res1_n, res1_n_as, by = "gene")
```

#graph logfoldchange of sense and Antisense
#"pairwise.complete.obs" must be used to throw out incomplete values - I know some authors will change every zero to 1 to fix this 
```{r}
grob1 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation: ",
            round(cor(
                Combinded_RES$log2FoldChange,
                Combinded_RES$AS_log2FoldChange,
                use = "pairwise.complete.obs"),
            4)
        ),
        x = 0.5,
        y = 0.97,
        hjust = 0,
        gp = gpar(col = 'blue', fontsize = 11, fontface = "bold")
    )
)

ggplot2::ggplot(data = Combinded_RES) + 
    ggplot2::geom_vline(xintercept = 0, alpha = .5) +
    ggplot2::geom_hline(yintercept = 0, alpha = .5) +
    ggplot2::geom_point(
        data = Combinded_RES,
        mapping = aes(y = log2FoldChange, x = AS_log2FoldChange),
        alpha = 4/10,
        size = 1,
        color = 'black'
    ) +
    ggplot2::geom_smooth(
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'forestgreen'
    ) +
    ggplot2::theme_bw() +
    ggplot2::annotation_custom(grob1) +
    ggplot2::geom_smooth(
        data = Combinded_RES,
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'blue'
    )
```


#check colnames of TPM_Nas_AS_up
```{r}
colnames(TPM_Nas_AS_up)
```

#rename AS table names so I can compare them with sense values - they will then have different names and labels 
```{r}
colnames(TPM_Nas_AS_up) <- c(
    "gene", "AS_baseMean", "AS_log2FoldChange", "AS_lfcSE", "AS_stat",
    "AS_pvalue", "AS_padj", "Avg_single_Tag_AS_TPM", "Avg_parental_AS_TPM"
)
```

#add sense expression info onto highest AS table so those points can be graphed seperately 
```{r}
High_up_AS <- merge(TPM_Nas_AS_up, res1_n, by = "gene")
```

#scatterplot now with high AS highlighted in red 
```{r}
grob2 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation Over Expressed AS: ",
            round(
                cor(
                    High_up_AS$log2FoldChange,
                    High_up_AS$AS_log2FoldChange,
                    use = "pairwise.complete.obs"),
                4
            )
        ),
        x= 0.3,
        y = 0.93,
        hjust = 0,
        gp = gpar(col = 'red', fontsize = 11, fontface = "bold")
    )
)

ggplot2::ggplot(data = Combinded_RES) + 
    ggplot2::geom_vline(xintercept = 0, alpha = .5) +
    ggplot2::geom_hline(yintercept = 0, alpha = .5) +
    ggplot2::geom_point(
        data = Combinded_RES,
        mapping = aes(y = log2FoldChange, x = AS_log2FoldChange),
        alpha = 4/10,
        size = 1,
        color = 'black'
    ) +
    ggplot2::geom_smooth(
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'forestgreen'
    ) +
    ggplot2::theme_bw() +
    ggplot2::geom_point(
        data = High_up_AS, 
        aes(y = log2FoldChange,x=AS_log2FoldChange), 
        color = "red",
        size =1, 
        alpha = 4/10
    )+
    ggplot2::annotation_custom(grob1) +
    ggplot2::annotation_custom(grob2) +
    ggplot2::geom_smooth(
        data = Combinded_RES,
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = 'blue'
    ) +
    ggplot2::geom_smooth(
        data = High_up_AS,
        aes(y = log2FoldChange, x = AS_log2FoldChange),
        method = lm,
        se = TRUE,
        color = "red"
    )
```


## now it is time to do grouped analysis :) 
#Make a new variable that's the same thing (need this to remove genes, so no gene is asigned to more than one group)
```{r}
DeSeq2_Sense_Results <- res1_n
```

#now we are binning genes by group of mRNA expression
#group 1 up genes
#group 5 down genes
```{r}
grp1_DeSeq2_Sense_Results <- dplyr::filter(res1_n, log2FoldChange > 2, padj < 0.05)
grp5_DeSeq2_Sense_Results <- dplyr::filter(res1_n, log2FoldChange < -2, padj < 0.05)
```

#group 3 "neutral"
```{r}
grp3_DeSeq2_Sense_Results <- dplyr::filter(res1_n, log2FoldChange < 1 , log2FoldChange >-1 )
```

#remove group 1 genes from DeSeq2_Sense_Results 
```{r}
DeSeq2_Sense_Results <- DeSeq2_Sense_Results[
    !(DeSeq2_Sense_Results$gene %in% grp1_DeSeq2_Sense_Results$gene), 
]
nrow(DeSeq2_Sense_Results) + nrow(grp1_DeSeq2_Sense_Results)
```

#remove group 5 genes from DeSeq2_Sense_Results 
```{r}
DeSeq2_Sense_Results <- DeSeq2_Sense_Results[
    !(DeSeq2_Sense_Results$gene %in% grp5_DeSeq2_Sense_Results$gene), 
]

nrow(DeSeq2_Sense_Results) +
nrow(grp1_DeSeq2_Sense_Results) +
nrow(grp5_DeSeq2_Sense_Results)

nrow(DeSeq2_Sense_Results)

nrow(grp1_DeSeq2_Sense_Results) + nrow(grp5_DeSeq2_Sense_Results)
```

#remove group 3 genes from DeSeq2_Sense_Results 
```{r}
DeSeq2_Sense_Results <- DeSeq2_Sense_Results[
    !(DeSeq2_Sense_Results$gene %in% grp3_DeSeq2_Sense_Results$gene), 
]
nrow(DeSeq2_Sense_Results)
nrow(grp3_DeSeq2_Sense_Results)
```

#sanity check count rows
```{r}
nrow(grp1_DeSeq2_Sense_Results) 
nrow(grp5_DeSeq2_Sense_Results)
```

#now from not group 1,3,5 - split into group 2 and 4
```{r}
grp2_DeSeq2_Sense_Results <- filter(DeSeq2_Sense_Results, log2FoldChange >=1)
grp4_DeSeq2_Sense_Results <- filter(DeSeq2_Sense_Results, log2FoldChange <= -1 )
```

#sanity check count rows
```{r}
nrow(grp1_DeSeq2_Sense_Results) +
nrow(grp5_DeSeq2_Sense_Results) +
nrow(grp3_DeSeq2_Sense_Results) +
nrow(grp2_DeSeq2_Sense_Results) +
nrow(grp4_DeSeq2_Sense_Results)
```

#sanity check count rows
```{r}
DeSeq2_Sense_Results <- DeSeq2_Sense_Results[
    !(DeSeq2_Sense_Results$gene %in% grp5_DeSeq2_Sense_Results$gene), 
]

nrow(DeSeq2_Sense_Results) +
nrow(grp1_DeSeq2_Sense_Results) +
nrow(grp5_DeSeq2_Sense_Results)

nrow(DeSeq2_Sense_Results)

nrow(grp1_DeSeq2_Sense_Results) + nrow(grp5_DeSeq2_Sense_Results)
```

#sanity check count rows
#only thing left in "DeSeq2_Sense_Results" is NA results 
```{r}
DeSeq2_Sense_Results <- DeSeq2_Sense_Results[
    !(DeSeq2_Sense_Results$gene %in% grp2_DeSeq2_Sense_Results$gene), 
]
DeSeq2_Sense_Results <- DeSeq2_Sense_Results[
    !(DeSeq2_Sense_Results$gene %in% grp4_DeSeq2_Sense_Results$gene), 
]
nrow(DeSeq2_Sense_Results)
```

#now we have groups by sense expression change
#now make table to integrate AS expression info 
```{r}
group1 <- merge(grp1_DeSeq2_Sense_Results, res1_n_as, by = "gene")
group2 <- merge(grp2_DeSeq2_Sense_Results, res1_n_as, by = "gene")
group3 <- merge(grp3_DeSeq2_Sense_Results, res1_n_as, by = "gene")
group4 <- merge(grp4_DeSeq2_Sense_Results, res1_n_as, by = "gene")
group5 <- merge(grp5_DeSeq2_Sense_Results, res1_n_as, by = "gene")
```

#what happens next is how i make violin plots. 
#I figured this out like a year ago and have copy pasted this code a bunch.
#this is the bins 
```{r}
mRNA1 <- data.frame(AS_log2FoldChange = group1$AS_log2FoldChange)
mRNA2 <- data.frame(AS_log2FoldChange = group2$AS_log2FoldChange)
mRNA3 <- data.frame(AS_log2FoldChange = group3$AS_log2FoldChange)
mRNA4 <- data.frame(AS_log2FoldChange = group4$AS_log2FoldChange)
mRNA5 <- data.frame(AS_log2FoldChange = group5$AS_log2FoldChange)
```

#now the little seperate bins are glued together 
```{r}
# Now, combine your two dataframes into one.  
# First make a new column in each that will be 
# a variable to identify where they came from later.
mRNA1$expression <- 'Over_Expressed'
mRNA2$expression <- 'Over_Expressed_not_sig'
mRNA3$expression <- 'Neutral'
mRNA4$expression <- 'Under_Expressed_not_sig'
mRNA5$expression <- 'Under_Expressed'

# and combine into your new data frame vegLengths
dataframe1 <- rbind(mRNA2, mRNA3, mRNA4, mRNA5, mRNA1)
```

#now we we make a violin plot 
```{r}
p <- ggplot2::ggplot(
    dataframe1,
    aes(factor(expression), fill = expression, AS_log2FoldChange)
) +
    ggplot2::geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
    ggplot2::scale_fill_manual(
        values = c("#9F9BA2", "limegreen", "#44A043", "#D1B3EC", "#825CA6")
    ) +
    ggplot2::theme_minimal()
p
```

#now we we make a violin plot with p values 
```{r}
my_comparisons <- list(
    c('Neutral', 'Over_Expressed'),
    c('Neutral', 'Under_Expressed'),
    c('Under_Expressed', 'Over_Expressed')
)

p <- ggplot2::ggplot(
    dataframe1,
    aes(factor(expression), fill = expression, AS_log2FoldChange)
) +
    ggplot2::geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
    ggplot2::scale_fill_manual(
        values = c("#9F9BA2", "limegreen", "#44A043", "#D1B3EC", "#825CA6")
    ) +
    ggplot2::theme_minimal() +
    ggpubr::stat_compare_means(comparisons = my_comparisons)
p
```

#now were going to do the same thing
#the bins stay the same
#but in the bins were are putting AS expression level 
```{r}
colnames(AS_TPM)[colnames(AS_TPM) == "name"] <- "gene"

TPM_group1 <- merge(grp1_DeSeq2_Sense_Results, AS_TPM, by = "gene")
TPM_group2 <- merge(grp2_DeSeq2_Sense_Results, AS_TPM, by = "gene")
TPM_group3 <- merge(grp3_DeSeq2_Sense_Results, AS_TPM, by = "gene")
TPM_group4 <- merge(grp4_DeSeq2_Sense_Results, AS_TPM, by = "gene")
TPM_group5 <- merge(grp5_DeSeq2_Sense_Results, AS_TPM, by = "gene")
```

```{r}
colnames(TPM_group1)
```

#making data frames to put into violin plot
```{r}
TPM_mRNA1 <- data.frame(AS_Level_Depletion = TPM_group1$Avg_single_Tag_AS_TPM)
TPM_mRNA2 <- data.frame(AS_Level_Depletion = TPM_group2$Avg_single_Tag_AS_TPM)
TPM_mRNA3 <- data.frame(AS_Level_Depletion = TPM_group3$Avg_single_Tag_AS_TPM)
TPM_mRNA4 <- data.frame(AS_Level_Depletion = TPM_group4$Avg_single_Tag_AS_TPM)
TPM_mRNA5 <- data.frame(AS_Level_Depletion = TPM_group5$Avg_single_Tag_AS_TPM)
```

#glueing the data frames into one 
```{r}
# Now, combine your two dataframes into one.  
# First make a new column in each that will be 
# a variable to identify where they came from later.
TPM_mRNA1$expression <- 'Over_Expressed'
TPM_mRNA2$expression <- 'Over_Expressed_not_sig'
TPM_mRNA3$expression <- 'Neutral'
TPM_mRNA4$expression <- 'Under_Expressed_not_sig'
TPM_mRNA5$expression <- 'Under_Expressed'

# and combine into your new data frame vegLengths
dataframe2 <- rbind(TPM_mRNA2, TPM_mRNA3, TPM_mRNA4, TPM_mRNA5, TPM_mRNA1)
```

#make violin plot
```{r}
my_comparisons <- list(
    c('Neutral', 'Over_Expressed'),
    c('Neutral', 'Under_Expressed'),
    c('Under_Expressed', 'Over_Expressed')
)

p2 <- ggplot2::ggplot(
    dataframe2,
    aes(factor(expression), fill = expression, AS_Level_Depletion)
) + ggplot2::geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
    ggplot2::scale_fill_manual(
        values = c("#9F9BA2", "limegreen", "#44A043", "#D1B3EC", "#825CA6")
    ) +
    ggplot2::theme_minimal() +
    ggplot2::ylim(0, 100)
p2
```


#make violin plot but now with p values 
```{r}
my_comparisons <- list(
    c('Neutral', 'Over_Expressed'),
    c('Neutral', 'Under_Expressed'),
    c('Under_Expressed', 'Over_Expressed')
)

p2 <- ggplot2::ggplot(
    dataframe2,
    aes(factor(expression), fill = expression, AS_Level_Depletion)
) +
    ggplot2::geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
    ggplot2::scale_fill_manual(
        values = c("#9F9BA2", "limegreen", "#44A043", "#D1B3EC", "#825CA6")
    ) +
    ggplot2::theme_minimal() +
    ggpubr::stat_compare_means(comparisons = my_comparisons, method = "t.test")
p2
```


#now I am gating by fold change >2 instead of 4 
```{r}
ONE_AS_res1Nab3_up <- dplyr::filter(
    res1_n_as,
    AS_log2FoldChange > 1,
    AS_padj < 0.05
)
ONE_AS_res1Nab3_down <- dplyr::filter(
    res1_n_as,
    AS_log2FoldChange < -1,
    AS_padj < 0.05
)
```

#comparing between fold change >2 instead of 4 
```{r}
ONE_TPM_Nas_AS_up <- dplyr::inner_join(
    ONE_AS_res1Nab3_up, Cuttoff_TPM_AS, by = "gene"
)
nrow(TPM_Nas_AS_up)
nrow(ONE_TPM_Nas_AS_up)
```

#New Sense filters 
```{r}
ONE_res1Nab3_up <- dplyr::filter(res1_n, log2FoldChange > 1, padj < 0.05)
ONE_res1Nab3_down <- dplyr::filter(res1_n, log2FoldChange < -1, padj < 0.05)
```


#new functional RNA catagory 
```{r}
ONE_UpAS_DownMRNA <- inner_join(ONE_TPM_Nas_AS_up, ONE_res1Nab3_down, by = "gene")
nrow(ONE_UpAS_DownMRNA)
```

#write names only to file
#thanks for writing this Kris
```{r}
fileConn <- paste(p_txt, "functional_AS_test_list.txt", sep = "/")
writeLines(as.character(ONE_UpAS_DownMRNA$gene), fileConn)
close(fileConn)
```

#calc overlaps 
```{r}
ONE_UpAS_UpMRNA <- dplyr::inner_join(
    ONE_TPM_Nas_AS_up, ONE_res1Nab3_up, by = "gene"
)
nrow(ONE_UpAS_UpMRNA)
nrow(ONE_TPM_Nas_AS_up)
nrow(ONE_res1Nab3_up)
```


`#HERE`
##now I am going to compare to 5781 and 2 WT Q and G1
#so I gotta read that data in 
```{r}
WT_AS_diff_expression <- read.delim(
    paste(p_full, "res_order_AS_Diff_expression.txt", sep = "/"),
    header = TRUE
)
WT_TPM <- read.delim(
    paste(p_full, "WT_Q_G1_TPM.txt", sep = "/"),
    header = TRUE
)
```

#filtered by expression 
```{r}
WT_AS_UP_Q <- dplyr::filter(
    WT_AS_diff_expression,
    log2FoldChange > 1,
    padj < 0.05
)
```

#calc what high AS is 
```{r}
quantile(WT_TPM$Avg_Q_IP_ANTISense, probs = c(0.5, 0.75, 0.8,0.85, 0.9, 0.95)) 
```
#once again I am gating at 80% 
```{r}
WT_high_level_AS <- dplyr::filter(WT_TPM, Avg_Q_IP_ANTISense > 14)
```

#check colnames 
```{r}
colnames(WT_high_level_AS)
```


#merge high level and overexpressed 
```{r}
colnames(WT_AS_UP_Q)[colnames(WT_AS_UP_Q) == "name"] <- "gene"
WT_best_AS <- merge(WT_AS_UP_Q, WT_high_level_AS, by.x = "gene", by.y = "ensgene")
```

#now compare WT to Nab3 experiment 
```{r}
AS_tested <- merge(WT_best_AS, High_up_AS, by = "gene")
nrow(AS_tested)
nrow(WT_best_AS)
nrow(High_up_AS)
```

#of the 180 above - how many are functional?
```{r}
Functional_WT <- merge(ONE_UpAS_DownMRNA, AS_tested, by = "gene")
nrow(Functional_WT)
nrow(ONE_UpAS_DownMRNA)
nrow(AS_tested)

```

#making a list of gene names from Nab3 data so I can highlight those genes in WT data 
```{r}
ONE_UpAS_DownMRNA_list <- ONE_UpAS_DownMRNA %>% dplyr::select(c("gene"))
ONE_TPM_Nas_AS_up_list <- ONE_TPM_Nas_AS_up %>% dplyr::select(c("gene"))

nrow(ONE_UpAS_DownMRNA_list)
nrow(ONE_TPM_Nas_AS_up_list)
```
#all genes in WT - AS tpm and differential expression 
```{r}
colnames(WT_AS_diff_expression)[
    colnames(WT_AS_diff_expression) == "name"
] <- "gene"
WT_data <- merge(WT_TPM, WT_AS_diff_expression, by.x = "ensgene", by.y = "gene")
```

#graph it and it is a blob 
```{r}
grob7 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation : ",
            round(
                cor(
                    WT_data$Avg_Q_IP_ANTISense,
                    WT_data$log2FoldChange,
                    use = "pairwise.complete.obs"
                ),
                4
            )
        ),
        x = 0.5,
        y = 0.97,
        hjust = 0,
        gp = gpar(col = "black", fontsize = 11, fontface = "bold")
    )
)


ggplot2::ggplot(data = WT_data) + 
    ggplot2::geom_point(
        mapping = aes(y = log2FoldChange, x= Avg_Q_IP_ANTISense),
        alpha = 2/10,
        size = 1
    ) +
    ggplot2::scale_x_continuous(
        trans = log2_trans(),
        breaks = trans_breaks("log2", function(x) 2^x),
        labels = trans_format("log2", math_format(2^.x))
    ) +
    ggplot2::theme_bw()+
    ggplot2::annotation_custom(grob7)+
    ggplot2::geom_smooth(
        data = WT_data,
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense),
        method = lm,
        se = TRUE,
        color = 'black'
    )
```



#map Nab3 genes to WT data 
```{r}
WT_data_Nab3_Sensitive_genes <- merge(
    WT_data, ONE_TPM_Nas_AS_up_list, by.x = "ensgene", by.y = "gene"
)

WT_data_functional_AS <- merge(
    WT_data, ONE_UpAS_DownMRNA_list, by.x = "ensgene", by.y = "gene"
)
```

#graph it and it is 2 blobs!! :) 
```{r}
grob8 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation : ",
            round(
                cor(
                    WT_data_Nab3_Sensitive_genes$Avg_Q_IP_ANTISense,
                    WT_data_Nab3_Sensitive_genes$log2FoldChange,
                    use = "pairwise.complete.obs"
                ),
                4
            )
        ),
        x = 0.5,
        y = 0.93,
        hjust = 0,
        gp = gpar(col = '#D37538', fontsize = 11, fontface = "bold")
    )
)

ggplot2::ggplot(data = WT_data) + 
    ggplot2::geom_point(
        mapping = aes(y = log2FoldChange, x= Avg_Q_IP_ANTISense),
        alpha = 2/10,
        size = 1
    ) +
    ggplot2::scale_x_continuous(
        trans = log2_trans(),
        breaks = trans_breaks("log2", function(x) 2^x),
        labels = trans_format("log2", math_format(2^.x))) +
    ggplot2::theme_bw() +
    ggplot2::annotation_custom(grob7)+
    ggplot2::annotation_custom(grob8)+
    ggplot2::geom_point(data = WT_data_Nab3_Sensitive_genes, 
             aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense), 
             color = '#D37538',
             size = 1) +
    ggplot2::geom_smooth(
        data = WT_data,
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense),
        method = lm,
        se = TRUE,
        color = 'black'
    ) +
    ggplot2::geom_smooth(
        data = WT_data_Nab3_Sensitive_genes,
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense),
        method = lm,
        se = TRUE,
        color ='#D37538'
    )
```

#graph it again 
```{r}
grob9 <- grid::grobTree(
    grid::textGrob(
        paste(
            "Pearson Correlation :",
            round(
                cor(
                    WT_data_functional_AS$Avg_Q_IP_ANTISense,
                    WT_data_functional_AS$log2FoldChange,
                    use = "pairwise.complete.obs"
                ),
                4
            )
        ),
        x = 0.5,
        y = 0.93,
        hjust = 0,
        gp = grid::gpar(col = 'red', fontsize = 11, fontface = "bold")
    )
)


ggplot2::ggplot(data = WT_data) + 
    ggplot2::geom_point(mapping = aes(y= log2FoldChange, x= Avg_Q_IP_ANTISense), alpha = 2/10, size = 1) +
    ggplot2::scale_x_continuous(
        trans = log2_trans(),
        breaks = trans_breaks("log2", function(x) 2^x),
        labels = trans_format("log2", math_format(2^.x))
    ) +
    ggplot2::theme_bw()+
    ggplot2::annotation_custom(grob7)+
    ggplot2::annotation_custom(grob9)+
    ggplot2::geom_point(
        data = WT_data_functional_AS, 
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense), 
        color ='red',
        size = 1
    ) +
    ggplot2::geom_smooth(
        data = WT_data,
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense),
        method = lm,
        se = TRUE,
        color='black'
    ) +
    ggplot2::geom_smooth(
        data = WT_data_functional_AS,
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense),
        method = lm,
        se = TRUE,
        color = 'red'
    )
```

#all on one. busy, very hard to look at. 
```{r}
ggplot2::ggplot(data = WT_data) + 
    ggplot2::geom_point(mapping = aes(y= log2FoldChange, x= Avg_Q_IP_ANTISense), alpha = 2/10, size = 1) +
    ggplot2::scale_x_continuous(
        trans = log2_trans(),
        breaks = trans_breaks("log2", function(x) 2^x),
        labels = trans_format("log2", math_format(2^.x))
    ) +
    ggplot2::theme_bw()+
    ggplot2::geom_point(
        data = WT_data_Nab3_Sensitive_genes, 
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense), 
        color = '#D37538',
        size = 1
    ) +
    ggplot2::geom_point(
        data = WT_data_functional_AS, 
        aes(y = log2FoldChange, x = Avg_Q_IP_ANTISense), 
        color = 'red',
        size = 1
    )
```
